Rocky Shore Tanaids Compostion
Chih-Lin Wei
2024-01-16
# library(devtools)
# install_github('AnneChao/iNEXT.3D')
## import packages
library(iNEXT.3D)
library(readxl)
library(reshape2)
library(doBy)
library(dplyr)
library(plyr)
library(stringr)
library(ggplot2)
library(vegan)
library(ggdendro)
library(patchwork)ggplot theme setting
large <- theme(legend.title = element_text(size=15),
legend.text = element_text(size=15),
axis.title = element_text(size=15),
axis.text = element_text(size=15),
strip.text = element_text(size=15))
rotate <- theme(axis.text.x = element_text(angle = 90, hjust=1))
no_strip <- theme(strip.background = element_rect(colour=NA, fill=NA),
strip.text = element_text(colour=NA))Preparing tanaid data
ta0 <- read_excel("../Data/Tanaidacea community.xlsx", sheet = 3)
ta <- ta0[, 1:2]
# Create factor "Microhabitat"
ta$Microhabitat <- str_replace(ta0$Microhabitat, " \\s*\\([^\\)]+\\)", "")
# Create factor "Replicate"
ta$Replicate <- str_extract(ta0$Microhabitat, "(?<=\\()\\d+(?=\\))")
ta <- cbind(ta, ta0[,-1:-3])
# Remove zero rows
ta <- ta[rowSums(ta0[,-1:-3])!=0, ]
ta$Site <- factor(ta$Site, levels=c("Shitiping", "Jihuei", "Jialulan"), labels = c("A", "B", "C"))
ta$Season <- factor(ta$Season, levels = c("SP", "SU"), labels = c("4", "8"))
names(ta)[2] <- "Month"
# Only keep data for analysis
b <- ta[, -1:-4]
rownames(b) <- with(ta, paste(Site, Month, Microhabitat, Replicate, sep="_"))Prepare CWB data
library(rjson)
library(dplyr)
cwb <- fromJSON(file="../Data/C-B0050-001.json")
# Meta data
meta <- lapply(cwb$cwbdata$Resources$Resource$Data$SeaSurfaceObs$Location, FUN=function(x)x[[1]])
# Monthly temperature
dat <- lapply(cwb$cwbdata$Resources$Resource$Data$SeaSurfaceObs$Location, FUN=function(x)x[[2]]$Monthly)
MonthlyTemperatureFun <- function(i){
cbind(StationNameEN = lapply(meta, FUN=function(x)x$StationNameEN)[i] %>% as.character %>% as.factor,
cbind(
StationLatitude=lapply(meta, FUN=function(x)x$StationLatitude)[i] %>% as.numeric,
StationLongitude=lapply(meta, FUN=function(x)x$StationLongitude)[i] %>% as.numeric,
DataMonth=lapply(dat[[i]], FUN=function(x)x$DataMonth) %>% unlist %>% as.numeric,
Maximum=lapply(dat[[i]], FUN=function(x)x$SeaTemperature$Maximum) %>% unlist %>% as.numeric,
MaximumYear=lapply(dat[[i]], FUN=function(x)x$SeaTemperature$MaximumYear) %>% unlist %>% as.numeric,
Mean=lapply(dat[[i]], FUN=function(x)x$SeaTemperature$Mean) %>% unlist %>% as.numeric,
Minimum=lapply(dat[[i]], FUN=function(x)x$SeaTemperature$Minimum) %>% unlist %>% as.numeric,
MinimumYear=lapply(dat[[i]], FUN=function(x)x$SeaTemperature$MinimumYear) %>% unlist %>% as.numeric,
MinimumAnomaly=lapply(dat[[i]], FUN=function(x)x$SeaTemperature$MinimumAnomaly) %>% unlist %>% as.numeric,
MaximumAnomaly=lapply(dat[[i]], FUN=function(x)x$SeaTemperature$MaximumAnomaly) %>% unlist %>% as.numeric,
MeanAnomaly=lapply(dat[[i]], FUN=function(x)x$SeaTemperature$MeanAnomaly) %>% unlist %>% as.numeric
) %>% as.data.frame
)
}
# Hualien Buoy
hb <- MonthlyTemperatureFun(18) %>% subset((DataMonth=="4"|DataMonth=="8") & MinimumYear == "2012" & MaximumYear == "2012")
# Chenggong
cg <- MonthlyTemperatureFun(8) %>% subset((DataMonth=="4"|DataMonth=="8") & MinimumYear == "2012" & MaximumYear == "2012")
# Taitung Buoy
tb <- MonthlyTemperatureFun(34) %>% subset((DataMonth=="4"|DataMonth=="8") & MinimumYear == "2012" & MaximumYear == "2012")
tmp <- rbind(hb, cg, tb)Aggregate by microhabitat
ta2 <- aggregate(b, by = list(ta$Site, ta$Month), FUN=sum)
names(ta2)[1:2] <- c("Site", "Month")
b2 <- ta2[, -1:-2]
rownames(b2) <- with(ta2, paste(Site, Month, sep="_"))Estimate Hill Numbers
- Bootstrap confidence interval (1000 iterations)
q0 <- iNEXT3D(t(b2), diversity = "TD", q = 0, nboot=1000)
q1 <- iNEXT3D(t(b2), diversity = "TD", q = 1, nboot=1000)
q2 <- iNEXT3D(t(b2), diversity = "TD", q = 2, nboot=1000)
#save(list=c("q0", "q1", "q2"), file="../Data/HillNumbersTD.rda")load("../Data/HillNumbersTD.rda")
h <- rbind(q0$iNextEst$size_based,
q1$iNextEst$size_based,
q2$iNextEst$size_based)
h <- cbind(h, ta2[match(h$Assemblage, rownames(b2)), 1:2])
h$Month <- factor(h$Month, levels=c("4", "8"))Fig. X. Size-based diversity accumulation curves based on spe richness (left), effective number of typical species (middle) and effective number of dominant species (right). The blue lines indicate the interpolated (rarefied) and red lines indicate extrapolated parts of the accumulation curves based on 1000 permutations. Dotted symbols indicate the observed diversity values.
# Minimum sample coverage for each order q
min(ldply(lapply(splitBy(~Order.q+Assemblage, subset(h, Order.q==0)), FUN=function(x)x[dim(x)[1],]))$SC)## [1] 0.9957578min(ldply(lapply(splitBy(~Order.q+Assemblage, subset(h, Order.q==1)), FUN=function(x)x[dim(x)[1],]))$SC)## [1] 0.9957578min(ldply(lapply(splitBy(~Order.q+Assemblage, subset(h, Order.q==2)), FUN=function(x)x[dim(x)[1],]))$SC)## [1] 0.9957578# SC = 0.99h1 <- subset(h, Order.q ==0 & SC <= 0.9957578)
p1 <- ggplot()+
geom_ribbon(data=h1,
aes(x=m, ymin=qD.LCL, ymax=qD.UCL, group=Assemblage, fill=Site), alpha=0.2)+
geom_line(data= subset(h1, Method=="Rarefaction"),
aes(x=m, y=qD, group=Assemblage, colour=Site), size=0.8)+
geom_line(data= subset(h1, Method=="Extrapolation"),
aes(x=m, y=qD, group=Assemblage, colour=Site), linetype=2, size=0.8)+
geom_point(data= subset(h1, Method=="Observed"),
aes(x=m, y=qD, group=Assemblage))+
scale_fill_viridis_d()+
scale_color_viridis_d()+
facet_wrap(~Month, scale="free")+
labs(x = "Numbers of Individuals", y = expression(Species~Richness~(""^0*italic(D))),
colour="Site")+
theme_bw()%+replace% large %+replace% theme(axis.text.x = element_blank(), axis.title.x = element_blank())h2 <- subset(h, Order.q ==1 & SC <= 0.9957578)
p2 <- ggplot()+
geom_ribbon(data=h2,
aes(x=m, ymin=qD.LCL, ymax=qD.UCL, group=Assemblage, fill=Site), alpha=0.2)+
geom_line(data= subset(h2, Method=="Rarefaction"),
aes(x=m, y=qD, group=Assemblage, colour=Site), size=0.8)+
geom_line(data= subset(h2, Method=="Extrapolation"),
aes(x=m, y=qD, group=Assemblage, colour=Site), linetype=2, size=0.8)+
geom_point(data= subset(h2, Method=="Observed"),
aes(x=m, y=qD, group=Assemblage))+
scale_fill_viridis_d()+
scale_color_viridis_d()+
facet_wrap(~Month, scale="free")+
labs(x = "Numbers of Individuals", y = expression(Species~Richness~(""^1*italic(D))),
colour="Site")+
theme_bw()%+replace% large %+replace% theme(legend.position = "none")p1/p2
p3 <- ggplot()+
geom_ribbon(data=h1,
aes(x=SC, ymin=qD.LCL, ymax=qD.UCL, group=Assemblage, fill=Site), alpha=0.2)+
geom_line(data= subset(h1, Method=="Rarefaction"),
aes(x=SC, y=qD, group=Assemblage, colour=Site), size=0.8)+
geom_line(data= subset(h1, Method=="Extrapolation"),
aes(x=SC, y=qD, group=Assemblage, colour=Site), linetype=2, size=0.8)+
geom_point(data= subset(h1, Method=="Observed"),
aes(x=SC, y=qD, group=Assemblage))+
scale_fill_viridis_d()+
scale_color_viridis_d()+
facet_wrap(~Month, scale="free")+
labs(x = "Sample Completeness", y = expression(Species~Richness~(""^0*italic(D))),
colour="Site")+
theme_bw()%+replace% large %+replace% theme(axis.text.x = element_blank(), axis.title.x = element_blank())p4 <- ggplot()+
geom_ribbon(data=h2,
aes(x=SC, ymin=qD.LCL, ymax=qD.UCL, group=Assemblage, fill=Site), alpha=0.2)+
geom_line(data= subset(h2, Method=="Rarefaction"),
aes(x=SC, y=qD, group=Assemblage, colour=Site), size=0.8)+
geom_line(data= subset(h2, Method=="Extrapolation"),
aes(x=SC, y=qD, group=Assemblage, colour=Site), linetype=2, size=0.8)+
geom_point(data= subset(h2, Method=="Observed"),
aes(x=SC, y=qD, group=Assemblage))+
scale_fill_viridis_d()+
scale_color_viridis_d()+
facet_wrap(~Month, scale="free")+
labs(x = "Sample Completeness", y = expression(Species~Richness~(""^1*italic(D))),
colour="Site")+
theme_bw()%+replace% large %+replace% theme(legend.position = "none")p3/p4
fr <- strsplit(q0$DataInfo$Assemblage, split="_") %>% ldply
names(fr) <- c("Site", "Month")
fr$Month <- factor(fr$Month, levels=c("4", "8"))
h1_out <- lapply(splitBy(~Assemblage, h1), FUN=function(x)tail(x, n=1)) %>% ldply
h2_out <- lapply(splitBy(~Assemblage, h2), FUN=function(x)tail(x, n=1)) %>% ldplyt0 <- cbind(cbind(fr, S.obs = q0$DataInfo$S.obs), tmp[c(1, 3, 5, 2, 4, 6),])
t0$Site <- factor(t0$Site, levels=c("A", "B", "C"))
t1 <- cbind(h1_out, tmp[c(1, 3, 5, 2, 4, 6),])
t2 <- cbind(h2_out, tmp[c(1, 3, 5, 2, 4, 6),])
h2 <- readRDS("../Data/H2.rds")
h2$Site <- factor(h2$Site, levels=c("A", "B", "C"))pv <- round(summary(lm(S.obs~Mean, data=t0))$coefficients[2, 4], 3)
r2 <- round(summary(lm(S.obs~Mean, data=t0))$adj.r.squared, 2)
f <- round(summary(lm(S.obs~Mean, data=t0))$fstatistic[1], 2)
label = paste0("italic(F)['1,4'] ==", f, "*','~P==", pv, "*','~italic(R)^2 ==", r2)
p7 <- ggplot(data=t0, aes(x=Mean, y=S.obs, shape=Month, colour=Site))+
geom_point(size=2, stroke=1.5)+
stat_smooth(formula=y~x, method = "glm", aes(group=1), alpha=0.2, colour="gray", linetype=2)+
annotate(geom = "text", x = 24, y = 12.5, hjust = 0, vjust = 1, label = label, parse = TRUE) +
scale_color_viridis_d()+
scale_shape_manual(values=c(1,19))+
labs(x = expression(Temperature~(degree~C)), y = "Numbers of Species", title="a", shape="Month")+
theme_bw() %+replace% large %+replace% theme(axis.text.x = element_blank(), axis.title.x = element_blank(), legend.position = "none")pv <- round(summary(lm(qD~Mean, data=t1))$coefficients[2, 4], 3)
r2 <- round(summary(lm(qD~Mean, data=t1))$adj.r.squared, 2)
f <- round(summary(lm(qD~Mean, data=t1))$fstatistic[1], 2)
label = paste0("italic(F)['1,4'] ==", f, "*','~P==", pv, "*','~italic(R)^2 ==", r2)
p8 <- ggplot(data=t1, aes(x=Mean, y=qD, ymin=qD.LCL, ymax=qD.UCL, shape=Month, colour=Site))+
geom_point(size=2, stroke=1.5)+
geom_errorbar()+
stat_smooth(formula=y~x, method = "glm", aes(group=1), alpha=0.2, colour="gray", linetype=2)+
annotate(geom = "text", x = 24.5, y = 22, hjust = 0, vjust = 1, label = label, parse = TRUE) +
scale_color_viridis_d()+
scale_shape_manual(values=c(1,19))+
labs(x = expression(Temperature~(degree~C)), y = expression(Species~Richness~(""^0*italic(D))), title="b", shape="Month")+
theme_bw() %+replace% large %+replace% theme(axis.text.x = element_blank(), axis.title.x = element_blank())pv <- round(summary(lm(qD~Mean, data=t2))$coefficients[2, 4], 3)
r2 <- round(summary(lm(qD~Mean, data=t2))$adj.r.squared, 2)
f <- round(summary(lm(qD~Mean, data=t2))$fstatistic[1], 2)
label = paste0("italic(F)['1,4'] ==", f, "*','~P==", pv, "*','~italic(R)^2 ==", r2)
p9 <- ggplot(data=t2, aes(x=Mean, y=qD, ymin=qD.LCL, ymax=qD.UCL, shape=Month, colour=Site))+
geom_point(size=2, stroke=1.5)+
geom_errorbar()+
stat_smooth(formula=y~x, method = "glm", aes(group=1), alpha=0.2, colour="gray", linetype=2)+
annotate(geom = "text", x = 24, y = 6, hjust = 0, vjust = 1, label = label, parse = TRUE) +
scale_color_viridis_d()+
scale_shape_manual(values=c(1,19))+
labs(x = expression(Temperature~(degree~C)), y = expression(Shannon~Diversity~(""^1*italic(D))), title="c", shape="Month")+
theme_bw()%+replace% large %+replace% theme(legend.position = "none")pv <- round(summary(lm(H2~Mean, data=h2))$coefficients[2, 4], 3)
r2 <- round(summary(lm(H2~Mean, data=h2))$adj.r.squared, 2)
f <- round(summary(lm(H2~Mean, data=h2))$fstatistic[1], 2)
label = paste0("italic(F)['1,4'] ==", f, "*','~P==", pv, "*','~italic(R)^2 ==", r2)
p10<- ggplot(data=h2, aes(x=Mean, y=H2, ymin=X1, ymax=X2, shape=Month, colour=Site))+
geom_point(size=2, stroke=1.5)+
geom_errorbar()+
stat_smooth(formula=y~x, method = "glm", aes(group=1), alpha=0.2, colour="gray")+
annotate(geom = "text", x = 24.5, y = 0.95, hjust = 0, vjust = 1, label = label, parse = TRUE) +
scale_color_viridis_d()+
scale_shape_manual(values=c(1,19))+
labs(x = expression(Temperature~(degree~C)), y = "H2' index", title="d", shape="Month")+
theme_bw()%+replace% large %+replace% theme(legend.position = "none")(p7+p8)/(p9+p10)
p11 <- ggplot(data=t0, aes(x=Site, y=S.obs, fill=Month))+
geom_bar(stat = "identity", position=position_dodge())+
scale_fill_viridis_d()+
labs(x = "Site", y = "Numbers of Species", title="a", shape="Month")+
theme_bw() %+replace% large %+replace% theme(axis.text.x = element_blank(), axis.title.x = element_blank(), legend.position = "none")
p12 <- ggplot(data=t1, aes(x=Site, y=qD, ymin=qD.LCL, ymax=qD.UCL, fill=Month))+
geom_bar(stat = "identity", position=position_dodge())+
geom_errorbar(stat = "identity", position=position_dodge(), linetype=2)+
scale_fill_viridis_d()+
labs(x = expression(Temperature~(degree~C)), y = expression(Species~Richness~(""^0*italic(D))), title="b", shape="Month")+
theme_bw() %+replace% large %+replace% theme(axis.text.x = element_blank(), axis.title.x = element_blank())
p13 <- ggplot(data=t2, aes(x=Site, y=qD, ymin=qD.LCL, ymax=qD.UCL, fill=Month))+
geom_bar(stat = "identity", position=position_dodge())+
geom_errorbar(stat = "identity", position=position_dodge(), linetype=2)+
scale_fill_viridis_d()+
labs(x = "Site", y = expression(Shannon~Diversity~(""^1*italic(D))), title="c", shape="Month")+
theme_bw()%+replace% large %+replace% theme(legend.position = "none")
p14<- ggplot(data=h2, aes(x=Site, y=H2, ymin=X1, ymax=X2, fill=Month))+
geom_bar(stat = "identity", position=position_dodge())+
geom_errorbar(stat = "identity", position=position_dodge(), linetype=2)+
scale_fill_viridis_d()+
labs(x = "Site", y = "H2' index", title="d", shape="Month")+
theme_bw()%+replace% large %+replace% theme(legend.position = "none")(p11+p12)/(p13+p14)
library(knitr)
kable(t0)| Site | Month | S.obs | StationNameEN | StationLatitude | StationLongitude | DataMonth | Maximum | MaximumYear | Mean | Minimum | MinimumYear | MinimumAnomaly | MaximumAnomaly | MeanAnomaly | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | A | 4 | 7 | Hualien Buoy | 24.03 | 121.63 | 4 | 28.0 | 2012 | 25.5 | 22.9 | 2012 | -1.8 | 3.3 | 0.8 |
| 64 | B | 4 | 9 | Chenggong | 23.10 | 121.38 | 4 | 25.7 | 2012 | 24.0 | 22.7 | 2012 | -2.6 | 0.4 | -1.3 |
| 32 | C | 4 | 10 | Taitung Buoy | 22.72 | 121.14 | 4 | 29.0 | 2012 | 26.6 | 24.9 | 2012 | -1.0 | 3.1 | 0.7 |
| 121 | A | 8 | 5 | Hualien Buoy | 24.03 | 121.63 | 8 | 31.1 | 2012 | 29.2 | 25.3 | 2012 | -3.2 | 2.6 | 0.7 |
| 68 | B | 8 | 11 | Chenggong | 23.10 | 121.38 | 8 | 29.5 | 2012 | 28.1 | 26.9 | 2012 | -1.6 | 1.0 | -0.4 |
| 36 | C | 8 | 8 | Taitung Buoy | 22.72 | 121.14 | 8 | 30.0 | 2012 | 27.9 | 25.5 | 2012 | -2.9 | 1.6 | -0.5 |
kable(t1)| .id | Assemblage | m | Method | Order.q | qD | qD.LCL | qD.UCL | SC | SC.LCL | SC.UCL | Site | Month | StationNameEN | StationLatitude | StationLongitude | DataMonth | Maximum | MaximumYear | Mean | Minimum | MinimumYear | MinimumAnomaly | MaximumAnomaly | MeanAnomaly | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | A_4 | A_4 | 211 | Rarefaction | 0 | 6.365286 | 5.627048 | 7.103523 | 0.9956038 | 0.9926322 | 0.9985754 | A | 4 | Hualien Buoy | 24.03 | 121.63 | 4 | 28.0 | 2012 | 25.5 | 22.9 | 2012 | -1.8 | 3.3 | 0.8 |
| 64 | B_4 | B_4 | 298 | Extrapolation | 0 | 9.517606 | 7.651172 | 11.384039 | 0.9956365 | 0.9896097 | 1.0000000 | B | 4 | Chenggong | 23.10 | 121.38 | 4 | 25.7 | 2012 | 24.0 | 22.7 | 2012 | -2.6 | 0.4 | -1.3 |
| 32 | C_4 | C_4 | 444 | Rarefaction | 0 | 7.611364 | 6.480387 | 8.742340 | 0.9954990 | 0.9936961 | 0.9973019 | C | 4 | Taitung Buoy | 22.72 | 121.14 | 4 | 29.0 | 2012 | 26.6 | 24.9 | 2012 | -1.0 | 3.1 | 0.7 |
| 121 | A_8 | A_8 | 138 | Extrapolation | 0 | 5.282364 | 3.289271 | 7.275457 | 0.9956636 | 0.9855805 | 1.0000000 | A | 8 | Hualien Buoy | 24.03 | 121.63 | 8 | 31.1 | 2012 | 29.2 | 25.3 | 2012 | -3.2 | 2.6 | 0.7 |
| 68 | B_8 | B_8 | 1428 | Extrapolation | 0 | 14.932366 | 9.506878 | 20.357854 | 0.9957578 | 0.9924121 | 0.9991035 | B | 8 | Chenggong | 23.10 | 121.38 | 8 | 29.5 | 2012 | 28.1 | 26.9 | 2012 | -1.6 | 1.0 | -0.4 |
| 36 | C_8 | C_8 | 352 | Rarefaction | 0 | 6.880689 | 5.686621 | 8.074758 | 0.9953146 | 0.9929312 | 0.9976980 | C | 8 | Taitung Buoy | 22.72 | 121.14 | 8 | 30.0 | 2012 | 27.9 | 25.5 | 2012 | -2.9 | 1.6 | -0.5 |
kable(t2)| .id | Assemblage | m | Method | Order.q | qD | qD.LCL | qD.UCL | SC | SC.LCL | SC.UCL | Site | Month | StationNameEN | StationLatitude | StationLongitude | DataMonth | Maximum | MaximumYear | Mean | Minimum | MinimumYear | MinimumAnomaly | MaximumAnomaly | MeanAnomaly | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 117 | A_4 | A_4 | 211 | Rarefaction | 1 | 1.883177 | 1.681757 | 2.084597 | 0.9956038 | 0.9925190 | 0.9986886 | A | 4 | Hualien Buoy | 24.03 | 121.63 | 4 | 28.0 | 2012 | 25.5 | 22.9 | 2012 | -1.8 | 3.3 | 0.8 |
| 64 | B_4 | B_4 | 298 | Extrapolation | 1 | 5.129220 | 4.604312 | 5.654129 | 0.9956365 | 0.9896115 | 1.0000000 | B | 4 | Chenggong | 23.10 | 121.38 | 4 | 25.7 | 2012 | 24.0 | 22.7 | 2012 | -2.6 | 0.4 | -1.3 |
| 32 | C_4 | C_4 | 444 | Rarefaction | 1 | 1.704697 | 1.604944 | 1.804450 | 0.9954990 | 0.9936266 | 0.9973714 | C | 4 | Taitung Buoy | 22.72 | 121.14 | 4 | 29.0 | 2012 | 26.6 | 24.9 | 2012 | -1.0 | 3.1 | 0.7 |
| 121 | A_8 | A_8 | 138 | Extrapolation | 1 | 2.936496 | 2.495292 | 3.377701 | 0.9956636 | 0.9859488 | 1.0000000 | A | 8 | Hualien Buoy | 24.03 | 121.63 | 8 | 31.1 | 2012 | 29.2 | 25.3 | 2012 | -3.2 | 2.6 | 0.7 |
| 68 | B_8 | B_8 | 1428 | Extrapolation | 1 | 3.509970 | 3.259010 | 3.760930 | 0.9957578 | 0.9921777 | 0.9993379 | B | 8 | Chenggong | 23.10 | 121.38 | 8 | 29.5 | 2012 | 28.1 | 26.9 | 2012 | -1.6 | 1.0 | -0.4 |
| 36 | C_8 | C_8 | 352 | Rarefaction | 1 | 3.880164 | 3.705037 | 4.055290 | 0.9953146 | 0.9928583 | 0.9977709 | C | 8 | Taitung Buoy | 22.72 | 121.14 | 8 | 30.0 | 2012 | 27.9 | 25.5 | 2012 | -2.9 | 1.6 | -0.5 |